Machine Learning, today, is being used almost everywhere: in self driving cars to identify the road, other cars, and hazards, in smartphones and laptops for facial recognition and filters and in weather prediction software and in stock markets to estimate what the next day will yield. These classifiers identify patterns in data, and use them to predict, interpret, and create new information at almost alarming accuracy. If you want to start learning how this technology works, then this is the class for you. In this course, students will learn the mathematics behind linear regression and classification, and will learn about more robust classifiers, such as Softmax functions and Support Vector Machines. Given time at the end of the course, students will also learn how these components come together to create Neural Networks the basic building block for the most powerful classifiers. Students will use these concepts to create an age estimation regression, a handwritten number interpreter, and, if time, a neural network based image classifier. All of the notes, along with solutions to the labs afterwords, will be made available to the students enrolled.

In this class, we'll start by teaching the basics of Regular Expressions: sequences of characters that search for patterns of letters and numbers. After that, we'll look at deterministic and non deterministic finite automata -- state machines -- to understand how computers identify and interpret these expressions and represent them as languages.